Audio signal enhancement by removing additive background noise from a corrupted noisy signal has recently received increased attention due to the prosperity of portable communication devices. Traditional methods of noise suppression include spectral subtraction, Wiener filtering, and a number of modifications on these methods that increase the intelligibility of the processed audio signal and/or reduce adverse artifacts.
A common model for a noisy signal, x(t), is a signal, s(t), plus additive noise, n(t), that is uncorrelated with the signalx(t)=s(t)+n(t).  (Equation 1)A common goal in noise suppression is to design a real-time system that generates some optimal estimate, ŝ(t), of s(t) from x(t). By assuming that the additive noise is stationary over a long time period relative to the short term non-stationary patterns of normal speech, an estimate of s(t) may be found in the frequency domain using spectral subtraction or Wiener filtering.
The basic Wiener gain is found from the power spectral densities (PSD) or from the estimated PSD as
                              H          ⁡                      (            ω            )                          =                                                            ϕ                s                            ⁡                              (                ω                )                                                                                      ϕ                  n                                ⁡                                  (                  ω                  )                                            +                                                ϕ                  s                                ⁡                                  (                  ω                  )                                                              .                                    (                  Equation          ⁢                                          ⁢          2                )            This can also be expressed as a function of the frequency dependent signal-to-noise ratio (SNR) as
                                                                                          H                  ⁡                                      (                    ω                    )                                                  =                                                                            Γ                      2                                        ⁡                                          (                      ω                      )                                                                            1                    +                                                                  Γ                        2                                            ⁡                                              (                        ω                        )                                                                                                        ,                                                                                          where                ⁢                                                                  ⁢                                                      Γ                    2                                    ⁡                                      (                    ω                    )                                                              =                                                                                          ϕ                      s                                        ⁡                                          (                      ω                      )                                                                                                  ϕ                      n                                        ⁡                                          (                      ω                      )                                                                      .                                                                        (                  Equation          ⁢                                          ⁢          3                )            
A bank of band-pass filters break the input noisy signal into sub-band signals. At each sub-band, the envelope detection of band-limited signal xk(t) is performed and averaged to provide a smooth estimation of the envelope and followed by noise level estimation. Gain computation at each band is performed using non-linear gain function with each band's a posteriori SNR. For simplicity, we abbreviate a posteriori SNR as SNR. The unmodified band-limited signal, xk(t), is then multiplied by the calculated gain to obtain the noise suppressed band-limited signal, ŝk(t). All of the ŝk(t) sub-band signals are summed to build the full-band signal estimate, ŝ(t), after the synthesis filter bank.
Although the current framework of immediately digitizing an incoming analog audio signal seems to be working well in current practice and is primarily driven by transistor scaling and flexibility of programmability. The increasing proliferation of portable electronics has increased emphasis on low-power systems. Functionality, and therefore the amount of signal processing possible, is often constrained primarily by a fixed power budget. Second, the system demands on current systems require very high resolution/high performance analog to digital (A/D) converters. Therefore, the resulting A/D converter block is often consuming a large fraction of the power budget, as well as system design time. Scaling will not help much in this case, since resolution has been increasing at approximately 1.5 bits/5 years at the same performance, and quickly running into additional physical limits which might further slow this progress.
For example, Adaptive Wiener filtering typically requires extensive computation time on current microprocessors which requires a lot of power (e.g., 4 million instructions per second). This is more power than a portable communication device, like a personal digital assistant (PDA) or other personal communication or computing device may be able to provide. Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.